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import numpy as np
def ptb_iterator(raw_data, batch_size, num_steps, steps_ahead=1):
"""Iterate on the raw PTB data.
This generates batch_size pointers into the raw PTB data, and allows
minibatch iteration along these pointers.
Args:
raw_data: one of the raw data outputs from ptb_raw_data.
batch_size: int, the batch size.
num_steps: int, the number of unrolls.
Yields:
Pairs of the batched data, each a matrix of shape [batch_size, num_steps].
The second element of the tuple is the same data time-shifted to the
right by one.
Raises:
ValueError: if batch_size or num_steps are too high.
"""
raw_data = np.array(raw_data, dtype=np.int32)
data_len = len(raw_data)
batch_len = data_len // batch_size
data = np.zeros([batch_size, batch_len], dtype=np.int32)
offset = 0
if data_len % batch_size:
offset = np.random.randint(0, data_len % batch_size)
for i in range(batch_size):
data[i] = raw_data[batch_len * i + offset:batch_len * (i + 1) + offset]
epoch_size = (batch_len - steps_ahead) // num_steps
if epoch_size == 0:
raise ValueError("epoch_size == 0, decrease batch_size or num_steps")
for i in range(epoch_size):
x = data[:, i*num_steps:(i+1)*num_steps]
y = data[:, i*num_steps+1:(i+1)*num_steps+steps_ahead]
yield (x, y)
if epoch_size * num_steps < batch_len - steps_ahead:
yield (data[:, epoch_size*num_steps : batch_len - steps_ahead], data[:, epoch_size*num_steps + 1:])
def shuffled_ptb_iterator(raw_data, batch_size, num_steps):
raw_data = np.array(raw_data, dtype=np.int32)
r = len(raw_data) % num_steps
if r:
n = np.random.randint(0, r)
raw_data = raw_data[n:n + len(raw_data) - r]
raw_data = np.reshape(raw_data, [-1, num_steps])
np.random.shuffle(raw_data)
num_batches = int(np.ceil(len(raw_data) / batch_size))
for i in range(num_batches):
data = raw_data[i*batch_size:min(len(raw_data), (i+1)*batch_size),:]
yield (data[:,:-1], data[:,1:])
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